A BaSiC tool for background and shading correction of optical microscopy images

Quantitative analysis of bioimaging data is often skewed by both shading in space and background variation in time. We introduce BaSiC, an image correction method based on low-rank and sparse decomposition which solves both issues. In comparison to existing shading correction tools, BaSiC achieves high-accuracy with significantly fewer input images, works for diverse imaging conditions and is robust against artefacts. Moreover, it can correct temporal drift in time-lapse microscopy data and thus improve continuous single-cell quantification. BaSiC requires no manual parameter setting and is available as a Fiji/ImageJ plugin. Accurate quantification of bioimaging data is often confounded by uneven illumination (shading) in space and background variation in time. Here the authors present BaSiC, a Fiji plugin solving both issues. It requires fewer input images and is more robust to artefacts than existing shading correction tools.